By Kurt Mehlhorn (auth.), Sudebkumar Prasant Pal, Kunihiko Sadakane (eds.)

ISBN-10: 331904656X

ISBN-13: 9783319046563

ISBN-10: 3319046578

ISBN-13: 9783319046570

This booklet constitutes the revised chosen papers of the eighth overseas Workshop on Algorithms and Computation, WALCOM 2014, held in Chennai, India, in February 2014. The 29 complete papers awarded including three invited talks have been conscientiously reviewed and chosen from sixty two submissions. The papers are prepared in topical sections on computational geometry, algorithms and approximations, dispensed computing and networks, graph algorithms, complexity and limits, and graph embeddings and drawings.

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**Additional info for Algorithms and Computation: 8th International Workshop, WALCOM 2014, Chennai, India, February 13-15, 2014, Proceedings**

**Example text**

Pn of points in P ordered increasingly by distance from a query point q ≥ Q; Vk := −∧, Sk := ∞; for i ⊗ 1 to n do if pi is not dominated by any point in Sk then insert pi to Sk ; Vk := max {Vk , f (pi )}; if |Sk | ∨ k then break; for j ⊗ i + 1 to n do if f (pj ) ∨ Vk then continue; if pj is not dominated by any point in Sk then insert pj to Sk ; delete a point p∩ in Sk such that f (p∩ ) = Vk ; update Vk ; return Sk ; Top-k Manhattan Spatial Skyline Queries 27 q Fig. 1. L1 circle centered at q, and expanding with increasing radius In the following, we explain how to sort P efficiently and how to break the tie for two data points that have the same distance from the chosen query q.

IEEE Computer Society (2003) 7. : An optimal and progressive algorithm for skyline queries. In: SIGMOD 2003: Proc. of the 2003 ACM SIGMOD International Conference on Management of Data, pp. 467–478. ACM (2003) 8. : The spatial skyline queries. In: VLDB 2006: Proc. of the 32nd International Conference on Very Large Data Bases, pp. 751–762, VLDB Endowment (2006) 9. : Spatial skyline queries: exact and approximation algorithms. GeoInformatica 15(4), 665–697 (2011) 10. : Reaching the top of the skyline: An efficient indexed algorithm for top-k skyline queries.

For each of the O(log n) candidate nodes, the corresponding instance of D outputs the distinct colors present in the points which that node contributes to the convex hull of P ∪ q. The set of colors returned by querying each candidate node can at most be of size c, where c is the total number of distinct colors in the convex hull of P ∪ q. However, the same color can be output by more than one candidate node. 2 which ensures that there are no duplicates in the ﬁnal output. Lemma 4. The data structure R answers range queries in O(log2 n + c log n) time.

### Algorithms and Computation: 8th International Workshop, WALCOM 2014, Chennai, India, February 13-15, 2014, Proceedings by Kurt Mehlhorn (auth.), Sudebkumar Prasant Pal, Kunihiko Sadakane (eds.)

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